Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 234

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Layered Double Hydroxide–Carbon Dot Composite: High-Performance Adsorbent for Removal of Anionic Organic Dye

Zhang, Manlin, Yao, Qingfeng, Lu, Chao, Li, Zenghe, Wang, Wenxing·2014·ACS Applied Materials & Interfaces

Abstract

It would be of significance to design a green composite for efficient removal of contaminants. Herein, we fabricated a facile and environmentally friendly composite via direct assembly of surface passivated carbon dots with abundant oxygen-containing functional groups on the surface of the positively charged layered double hydroxide (LDH). The resulting LDH-carbon dot composites were characterized by X-ray diffraction (XRD), Fourier transformed infrared (FTIR) spectroscopy, high resolution transmission electron microscopy (HRTEM), scanning electron microscopy (SEM), and N2 adsorption-desorption technique. The adsorption performances of the resulting LDH-carbon dot composites were evaluated for the removal of anionic methyl blue dye. Taking advantage of the combined benefits of LDH and carbon dots, the as-prepared composites exhibited high uptake capability of methyl blue (185 mg/g). The adsorption behavior of this new adsorbent fitted well with Langmuir isotherm and the pseudo-second-order kinetic model. The reasons for the excellent adsorption capacity of methyl blue on the surface of the LDH-carbon dot hybrid were further discussed. A probable mechanism was speculated to involve the cooperative contributions of hydrogen bonding between methyl blue and carbon dots and electrostatic attraction between methyl blue and LDH, in the adsorption process. This work is anticipated to open up new possibilities in fabricating LDH-carbon dot materials in dealing with anionic dye pollutants.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/am505765e

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

Metadata

Authors
Zhang, M., Yao, Q., Lu, C., Li, Z., & Wang, W.
Venue
ACS Applied Materials & Interfaces
Published
2014-10-23
Volume
6
Issue
22
Pages
20225-20233
ISSN
  • 1944-8244 (ISSN-L)
  • 1944-8252
OpenAlex
W2333644974
Cited by
234
References
2
FWCI
13.88
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.